INTELLIGENT INFORMATION DISSEMINATION IN VEHICULAR AD HOC NETWORKS

International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.2, No.1, March 2011
DOI : 10.5121/ijasuc.2011.2110 112
INTELLIGENT INFORMATION DISSEMINATION IN
VEHICULAR AD HOC NETWORKS
M. S. Kakkasageri1
and S. S. Manvi2
1
Department of Electronics and Communication Engineering,
Basaveshwar Engineering College, Bagalkot-587102, Karnataka, India
mahabalesh_sk@yahoo.co.in
2
Department of Electronics and Communication Engineering,
Reva Institute of technology and Management, Bangalore-560064, Karnataka, India
agentsun2002@yahoo.com
ABSTRACT
Vehicular Ad hoc Networks (VANETs) are a compelling application of ad hoc networks, because of the
potential to access specific context information (e.g. traffic conditions, service updates, route planning)
and deliver multimedia services (VOIP, in-car entertainment, instant messaging, etc.). In this paper, we
propose an agent based intelligent information dissemination model for VANETs. Safety information like
cooperative driving, accident, road condition warnings, etc. play a major role for applications of VANET.
Safety information dissemination poses a major challenge of delay-sensitive nature. This paper proposes
an agent based model for intelligent information dissemination in VANETs. Proposed model uses
cognitive agent concept for realizing intelligent information dissemination. To test the efficiency of the
model, proposed scheme is simulated using NS-2 simulator. Some of the performance parameters
analyzed are bandwidth utilized, push latency and push/pull decision latency.
KEYWORDS
Vehicular ad hoc networks, Cognitive agents, Push-pull concept
1. INTRODUCTION
A Mobile Ad-hoc Network (MANET) is comprised of a group of mobile nodes, which have the
capability of self-organization in a decentralized fashion and without fixed infrastructure.
VANETs are special case of MANETs. The key differences as compared to MANET
environment are following: 1) Restricted mobility constraints 2) Extremely high mobility and
time-varying vehicle traffic density 3) Most of the vehicles provide sufficient computational and
power resources, thus eliminating the need for introducing complicated energy-aware
algorithms. 4) Vehicles will not be affected by the addition of extra weight for antennas and
additional hardware. Some parameters that have to be mainly concentrated in VANETs for
protocol design are extremely high mobility, restricted movements, fast topology changes and
time varying vehicle traffic density.
VANET raises several interesting issues with regard to Media access control (MAC), Mobility
management, Data aggregation, Data validation, Data dissemination, Routing, Network
Congestion, Performance analysis, Privacy and Security [1].

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• Mobility management: Since vehicles are highly mobile and change their point of
network attachment frequently while accessing Internet services through gateways, it is
advisable to have some mobility management schemes that take care of vehicle mobility
and provide seamless communication. Mobility management has to meet the following
requirements: seamless mobility (communication must be possible irrespective of
vehicle position), low handoff latency, support IP V6 and scalable overheads.
• Data aggregation: The vehicles have to pass on the data sent by the neighbors to other
neighbors of its coverage area. This increases the number of packets to be sent by a
vehicle. Therefore, data aggregation techniques are applied to reduce such overheads.
Data aggregation is an interesting approach, which reduces the number of packets
transmitted drastically by combining several messages related to the same event into
one aggregate message. For example, the records about two vehicles can be replaced by
a single record with little error, if the vehicles are very close to each other and move
with relatively the same speed.
• Data validation: A vehicle may send the data it has observed directly (assuming that a
vehicle always trusts the data it has gathered itself) to its neighbors. Sometimes
malicious vehicles may send the incorrect information to confuse the users. For
example, a malicious node may send the false accident information and divert all the
vehicles on other roads, which may some times lead to traffic congestion. In such
situation data validation techniques must be applied before passing on the received
information to other nodes.
• Data dissemination: Data dissemination can be defined as broadcasting information
about itself and the other vehicles it knows about. Each time a vehicle receives
information broadcasted by another vehicle, it updates its stored information
accordingly, and defers forwarding the information to the next broadcast period, at
which time it broadcasts its updated information. The dissemination mechanism should
be scalable, since the number of broadcast messages is limited, and they do not flood
the network. VANET characteristics like high-speed node movement, frequent topology
change, and short connection lifetime especially with multi-hop paths needs some
typical data dissemination models for VANETs.
• Routing: Since the topology of the network is constantly changing, the issue of routing
packets between any pair of nodes becomes a challenging task. Most protocols should
be based on reactive routing instead of proactive. Multicast routing is another challenge
because the multicast tree is no longer static due to the random movement of nodes
within the network.
• Network congestion: Congestion control in VANETs is a challenging issue. The
Internet is based on an end-to-end paradigm, where the transport protocol (e.g. TCP)
instances at the endpoints detect overload conditions at intermediate nodes. In case of
congestion, the source reduces its data rate. However, in VANETs the topology changes
within seconds and a congested node used for forwarding a few seconds ago might not
be used at all at the point in time when the source reacts to the congestion.
Some of the important applications of VANETs are message and file delivery, location-
dependent services, Internet connectivity, information and warning functions, co-operative
assistance systems, safety services (like emergency breaking, accidents, passing assistance,
security distance warning, etc.) traffic monitoring, etc.

114
2. RELATED WORKS
Several works reported in the literature deals with ad hoc networks and their applicability in
VANETs. Some of the works are as follows. The work given in [2] describes a system called as
Ad Hoc City, which is a multi tier wireless ad hoc network routing architecture for general-
purpose wide-area communication. The backbone network in this architecture is itself also a
mobile multihop network, composed of wireless devices mounted on mobile fleets such as city
buses or delivery vehicles.
The work given in [3] addresses the issues pertaining to medium access control schemes in
highly dynamic automotive networks that reduce latency and perform reliable communication.
It also describes a distributed positioning algorithm, called the kernel algorithm, suited for
asynchronous ad hoc wireless networks under complexity constraints. Traffic congestion
avoidance by disseminating traffic information through peer-to-peer networks based on WiFi
technology is studied in [4].
A mixed mode wireless LAN comprising of infrastructure and ad hoc mode operations is
presented in [5] where MANETs are connected through several base stations. The work given in
[6] presents an autonomous, self-organizing and decentralized configuration and management
system for a group of base stations in wireless networks. The individual base stations aggregate
and share network information. A distributed algorithm computes a local configuration at each
base station based on the shared information.
A dynamic clustering solution, which is distributed in nature, handles the cluster management
by taking into account practicalities like packet losses etc., and integrates with a routing module,
is presented in [7]. A security concept based on a distributed certification facility is described in
[8]. A network is divided into clusters with one cluster head node for each cluster. These cluster
head nodes execute administrative functions and hold shares of a network key used for
certification. The work given in [9] introduces a scalable service discovery protocol for
MANETs, which is based on the homogeneous and dynamic deployment of cooperating
directories within the network. A congestion control method with dynamic clustering for
variable topology and link qualities is discussed in [10].
Autonomous and cooperative collection of traffic jam statistics to estimate arrival time to
destination for each car using inter-vehicle communication is proposed in [11]. Optimal next-
hop selection in a route between two vehicles for a simple scenario of VANETs on a highway is
necessary to enhance the route lifetime [12]. In [13], problem of low-latency content
distribution (multicast streaming) to a dense vehicular highway network from roadside info-
stations, using efficient multi-hop vehicle-to-vehicle collaboration is explained. A model to
predict parking lot occupancy based on information exchanged among vehicles is discussed in
[14]. A data-gathering scheme for wireless sensor networks based on agent’s cooperation to deal
with the importance of the information is discussed in [15]. Agent cooperation aims to reduce
an important amount of the information communicated over the network by eliminating the
unimportant information and the inter-sensor-nodes redundancy. A priority-scheduling scheme
to increase the Quality of Service in VANETs is proposed in [16].
The problem addressed in the paper is information dissemination using software agents
including static cognitive and mobile agents. Vehicles autonomously collect, classify and
disseminate critical information with assistance of cognitive software agent. Rest of the paper is
organized as follows. Brief explanation of software agents is given in section 3. Proposed
intelligent information dissemination is presented in section 4. Simulation and the result
analysis are presented in section 5. Finally, section 6 concludes paper and explains future work.

115
3. SOFTWARE AGENTS
The traditional programming paradigm uses functions, procedures, structures and objects to
develop software for performing a given task. This paradigm does not support development of
flexible, intelligent and adaptable software and also does not facilitate all the requirements of
Component Based Software Engineering (CBSE). In recent developments, agent technology is
making its way as a new paradigm in the areas of artificial intelligence and computing which
facilitates sophisticated software development with features like flexibility, scalability and
CBSE requirements [17].
Agents are the autonomous programs activated on an agent platform of a host. The agents use
their own knowledge base to achieve the specified goals without disturbing the activities of the
host. They have two special properties: mandatory and orthogonal which make them different
from the standard programs. Mandatory properties are: autonomy, reactive, proactive and
temporally continuous. The orthogonal properties are: communicative, mobile, learning and
believable [18]. There are four different types of agents used in problem solving: local or user
interface agents, network agents, distributed AI (Artificial Intelligence) agents and mobile
agents. The network agents and user interface agents are single agent systems whereas others
are multiagent systems. Agents of single agent systems never cooperate or communicate with
each other but they can interact with the local or remote resources of the specified host.
Mobile agent is an itinerant agent consisting of program, data and execution state information,
migrates from one host to another host in a heterogeneous network and executes at a remote
host until it completes a given task [19]. By nature, mobile agents are flexible modular entities,
which can be created, deployed and deleted in real-time. The mobile code should be platform
independent, so that, it can execute at any remote host in a heterogeneous network environment.
Inter-agent communication can be achieved by message passing, RPC (Remote Procedure Call)
or common knowledge base (blackboard). A mobile agent platform comprises of agents, agent
server, interpreter and transport mechanisms. An agent server is responsible for receiving
mobile agents and sending it for execution by local interpreter.
Agents can be written in Java, Tcl, Perl and XML languages. An agent interpreter depends on
the type of agent script/ language used. An agent platform offers the following services: creation
of static and mobile agents, transport for mobile agents, security, communication messaging and
persistence. Some of the Java based agent platforms are: Aglets, Grasshopper, Concordia,
Voyager and Odyssey [20]. The agent based schemes comprising of static or mobile agents
offer several advantages as compared to traditional approaches: overcomes latency; reduces
network traffic; encapsulates protocols; flexibility; adaptability; software reusability and
maintainability; and facilitates creation of customized dynamic software architectures. The
multi-agent architecture of context-aware system and the learning scenarios within ubiquitous
learning environments is presented in [21]. This architecture is based on the system for sharing
public interest information and knowledge, which is accessible through always-on, context-
aware services.
A cognitive agent may be defined as a software entity, which functions continuously and
autonomously in a particular environment. Cognitive agent carries out activities in a flexible
and intelligent manner. It is responsive to changes in the environment. It learns from the
experience and can communicate and co-operate with other agents. It is proactive, exhibits
opportunistic, goal-oriented behaviour and takes the initiative when appropriate.
In agent systems, the basis for ascribing beliefs and intentions to their actions is not just
sentimental. Even though computational agents are pieces of machinery, their designs must be
specified and behaviours commanded by humans. And because humans think and speak using

116
cognitive terms such as beliefs, knowledge, desires and intentions, it is natural to use the same
cognitive concepts when constructing agents and assigning the tasks to them. An agent operates
in a physical or a computational environment. Some combination of the agent’s data structures
and program must reflect the information it has about its environment, so that the agent can
work properly according to the changing environment. Because this information would reflect
the state of the environment according to the agent, it can be termed its knowledge or a set of its
beliefs. Desires are the state of the environment the agent prefers. Intentions correspond to the
state of the environment the agent is trying to achieve, which should be a consistent subset of
the agent’s desires and should be directly connected to the agent’s actions.
4. INTELLIGENT INFORMATION DISSEMINATION IN VANETS
In this section, we describe network environment and cognitive agency for intelligent
information dissemination in VANETs.
4.1. Network environment
In this section, we introduce network environment used in the analysis of scheme for critical
information gathering and dissemination in VANETs. We consider a VANET in which N
numbers of vehicles are separated by the safety distance (between consecutive vehicles) Sd.
Proposed VANET is purely based on vehicle-to-vehicle (V2V) architecture, where both the
collection and the restitution of information are done within the VANET. We assume that
vehicles move in an urban road scenario as shown in figure 1. All vehicles are equipped with
General Positioning Systems (GPS) and on-board communication devices for communication.
Each vehicle is loaded with location digital map and is concerned about road information ahead
of it on its direction. Each vehicle communicates with other vehicle within its communication
range R. If the neighbor vehicle is not within communication range R, the vehicle delivers
critical data to the data center “D”, which may be associated with traffic light at cross junction.
All vehicles are assumed to have several sensors. Safety information in vehicle is determined by
particular set of sensors. For example, tyre pressure and vehicle speed sensors are responsible
for safety speed driving. Traffic density sensor, accident sensor (body pressure sensor) and
vehicle speed sensor together sense the occurrence of accident, which will be disseminated for
further decisions for driving.
4.2. Cognitive agency
Proposed agent framework for critical information gathering and dissemination is based on
push-pull concept. For critical events, proposed scheme takes appropriate decisions (push or
pull) as and when required. Using push approach, we can efficiently use bandwidth by
broadcasting only the critical events that are detected (like accidents, heavy rain, fog, etc.) to the
vehicles in VANET. In pull approach, noncritical applications (like road conditions, vehicle
speed, etc.,) can be stored within vehicle itself.
The framework comprises of cognitive information agency. Components of agency and their
interactions are depicted in figure 2. Agency consists of knowledge base (consists of critical
information details and running application(s) details), static agents and mobile agents. Static
agent is Vehicle Manager Agent (VMA). Mobile agents are Critical Information Push Agent
(CIPSA) and Critical Information Pull Agent (CIPLA).

117
Figure 1. Network Environment
• Knowledge Base (KB): It comprises of information of node ID, neighbors list, available
bandwidth for communication, node status (connected/disconnected to network), total
number of critical information available, information status (old/new) and push/pull
event of past and present critical information. KB is read or updated by VMA, CIPSA
and CIPLA.
• Vehicle Manager Agent: It is a static cognitive agent based on BDI model that runs in a
node, creates agents and knowledge base, controls and coordinates activities of
cognitive agency. This agent triggers CIPSA for pushing critical information, and
CIPLA for delivering pulled information to vehicles in network. Belief set, desire and
intention generation are part of VMA functions.
Belief set generation: VMA updates its beliefs according to sensed values from sensors
and also updates knowledge base. We assumed that sensor has two states: LOW and
HIGH. If sensor reading is greater than or equal to threshold value (assuming that
threshold value is decided by VMA), then sensor state is treated as HIGH, otherwise it
is LOW. VMA observes individual sensor states for a fixed time window T (where T
represents time duration from tx to tx′where tx′> tx). During this time window, out
of total transitions (Tlh), VMA counts number of LOW transitions (Tl) and HIGH
transitions (Th). Finally, VMA computes sensor state as either HIGH or LOW.
Desire and Intention generation: Desire and intention generation for an event is core
part of cognition capabilities of VMA. A determined desire (either push or pull) for
current captured sensor status is generated using belief, if and only if all conditions
leading to its generation are satisfied
Extraction of nearest matching event belief sets is as follows: If an event has been
happened, which is having ambiguity to match with contents in the belief. Extraction of
nearest matching event belief sets is done by comparing sensor states with sensor states
of previous values. From extracted event belief sets, segregate them based on actions
with push or pull. From knowledge base, reorganize segregated event belief sets for

118
push and pull actions, respectively. Joint density function for push and Pull is
computed. Based on these functions desire is generated for maximum value of push
and pull. Generated beliefs and desire are updated in knowledge base.
• Critical Information Push Agent (CIPSA): It is a mobile agent, employed to push
critical information to other vehicles in network. It is triggered by VMA that travels
around network by creating its clones (a clone is a similar copy of agent with different
destination addresses) and disseminates critical information. For each visited vehicle, it
updates KB in coordination with VMA of vehicle. It is assumed that all vehicles have
GPS facility and loaded with digital road map. VMA in each vehicle has knowledge of
road length, road width, and number of lanes and current position of the vehicle.
Operation sequences of CIPSA are as follows.
1. Whenever critical information is detected source vehicle VMA identifies it's
location based on the GPS.
2. VMA classifies the critical information dissemination area into four quadrants.
3. VMA considers dissemination area as a pair of quadrant considering source
vehicle position and direction of travel.
4. VMA triggers CIPSA to move in any one of the quadrant (dissemination area).
5. CIPSA delivers critical information to all vehicles in the dissemination area.
While delivering critical information to a vehicle, CIPSA also encapsulates
available critical information in visited vehicle for delivery to other vehicles.
6. Encapsulated critical information is delivered to all vehicles on the way.
7. Once the CIPSA finds boundary vehicle of dissemination area (assuming
vehicle has a mechanism to detect boundary of VANET based on GPS
coordinates with respect to source vehicle), CIPSA queries for data centers.
Data centers are usually attached with traffic lights, mobile towers etc.
8. CIPSA pours critical information to data centers and destroys itself.
• Critical Information Pull Agent (CIPLA): VMA triggers CIPLA when vehicle is
interested to retrieve latest information like fog, weather condition, etc. available in
other vehicles of a network. VMA knows relative positions of neighbor vehicles using
stored road maps, by mapping vehicle's latitude and longitude coordinates to points on
road in which vehicle is driving.
Operation sequences of CIPLA are as follows.
1. VMA triggers CIPLA by mentioning required information and destination.
2. With the help of GPS, VMA gives its upfront neighbors list to CIPLA (source
vehicle is interested in information available in forward direction).
3. From source vehicle, CIPLA moves in forward direction towards its required
destination hopping from one vehicle to another.
4. At each visited vehicle, CIPLA interacts with VMA.
5. If required information or nearest matching information is available in visited
vehicle, CIPLA sends back information to source vehicle. Otherwise, it updates
itself and moves forward by using vehicle positioning information (based on
GPS) till it reaches destination (based on GPS and Road maps). In this way
CIPLA roams around network.
6. Once CIPLA reaches a vehicle at destination, it traces back to source with
information picked at visited vehicles.
7. If CIPLA does not find a vehicle to reach vehicles at destination, it sends
available information to source and keeps waiting for certain duration around
place until finds a vehicle moving towards required destination. Similarly, it
performs waiting operation when moving towards source.

119
Figure 2. Cognitive agency
5. SIMULATION
5.1. Simulation Model
We have simulated proposed model by considering a Bangalore city map. The simulation is
carried out using NS-2.28 to test the performance and effectiveness of approach. We consider
“N” number of vehicles moving in a fixed region of length “A” Km. and breadth “B” Km. We
consider vehicles to move in number of lanes “L”. Communication coverage area for each
vehicle is considered as a “Vcom” meters.
At the beginning of the simulation, vehicles are uniformly distributed in lanes. This setting
holds under assumption that there is a free flow movement of vehicles, i.e. we do not account
for congestion that may arise in roads. It is assumed that all vehicles are equipped with a
communication device and knows start position, start time of vehicle, route that it selects, and
speed at which it travels.
Safety distance of “R” meters is maintained from preceding vehicle for a certain tolerance time,
and then change lane if possible. Changing lane allows vehicle to move to an adjacent lane if
there is space (safety distance) in that lane. At every intersection, we assume that each vehicle
can choose to make either a left or right (if not a one-way road) or no turn. Mobility factor for
each node is in between the range of “I” to “J” Kmph (Kilometers per hour). Border effect of
bounded simulation region on vehicle mobility is accounted for by making vehicle reappear in
the region.

120
5.2. Simulation Procedure
Simulation inputs are as follows: A= 5000m, B= 5000m, N= 50, Vcom = 300m, I= 10 Kmph, J=
100 Kmph, L= 2, R = 4 mts. Simulation procedure for proposed cognitive agent model is as
follows.
Begin
Generate VANET in given road length by placing vehicles uniformly.
Maintain a data structure at each vehicle to store information as specified by scheme.
Apply mobility to nodes.
Generate cognitive agency (agents are implemented as objects).
Compute performance of system.
End
5.3. Performance metrics
Some of performance metrics evaluated are Bandwidth utilized, Packet delivery ratio, Push
latency, Push/Pull decision latency.
• Bandwidth utilized: It is defined as the amount of bandwidth utilized out of the total
bandwidth available. It is expressed in terms of Mbps.
• Push Latency: It is total time taken by source vehicle to disseminate critical event
information to all vehicles in VANET. It is expressed in terms of milli seconds.
• Push/Pull decision latency: It is the total time taken by vehicle to decide the generated
event as Push or Pull. It is expressed in terms of microseconds.
5.4. Result Analysis
As shown in figure 3, for different mobility values like 40 Kmph, 60 Kmph, 80 Kmph and 100
Kmph, as the number of vehicles increase, the bandwidth utilization increases linearly.
Bandwidth utilization remains constant if VANET has constant number of vehicles on the road.
Push latency for the mobility values of 40 Kmph, 60 Kmph, 80 Kmph and 100 Kmph with
varying number of vehicles is depicted in figure 4.
As shown in figure 5, as the number of sensors increase, Push/Pull decision latency increases
linearly. For large number of sensors, Push/Pull decision latency remains constant. This is
because of the efficiency of VMA in computation to decide the generated event as either critical
or non-critical.
Figure 3. Bandwidth utilized Vs. Number of Vehicles

121
Figure 4. Push Latency Vs. Number of Vehicles
Figure 5. Push/Pull Decision Latency Vs. Number of Vehicles
6. CONCLUSIONS
There is a good future for applications of VANET, ranging from diagnostic, safety tools,
information services, and traffic monitoring and management to in-car digital entertainment and
business services. However, for these applications to become everyday reality, an array of
technological challenges needs to be addressed. In this paper, we have developed an agent-
based intelligent information dissemination in VANETs. Some of the issues like evaluation of
the cognitive agent based model overheads including memory, computational and
communication overhead, consideration of more number of lanes per road, etc., may considered
for future work.

122
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AUTHORS BIOGRAPHY
Mahabaleshwar S. Kakkasageri completed his B. E in Electronics and
Communication Engineering from Karnatak University Dharwad, India and
M.Tech from Visvesvaraya Technological University Belgaum, India. He is
pursuing his Ph.D in the area of Vehicular Ad hoc Networks (VANETs). Presently,
he is working as Faculty in Department of Electronics and Communication
Engineering, Basaveshwar Engineering College, Bagalkot, Karnataka, India. He
has published 16 national and international conference papers and 4 national and
international journals. Recently, he has coauthored a book on Wireless and Mobile
Networks: Concepts and Protocols, published by Wiley-India. His area of interest is
wireless networks, especially vehicular ad hoc networks, and sensor networks. He
is a member of IEEE USA, IETE India.
Sunilkumar S. Manvi received M.E. degree in Electronics from the University of
Visweshwariah College of Engineering, Bangalore and Ph.D degree in Electrical
Communication Engineering, Indian Institute of Science, Bangalore, India. He is
currently working as Dean (R&D) and Professor & Head of Department of
Electronics and Communication Engineering at Reva Institute of Technology and
Management, Bangalore, India. He is involved in research of Agent based
applications in Multimedia Communications, Grid computing, Vehicular Ad-hoc
networks, E-commerce and Mobile computing. He has published about 100 papers
in national and international conferences and 40 papers in national and international
journals. He has published 3 books. He is a Fellow IETE (FIETE, India), Fellow IE
(FIE, India) and member ISTE (MISTE, India), member of IEEE (MIEEE, USA),
He has been listed in Marqui's Who is Who in the World.

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